Make the prediction grid

Intro

Make an evenly spaced UTM prediction grid with all spatially varying covariates for the diet and the biomass data

library(tidyverse)
library(tidync)
library(tidyterra)
library(tidylog)
library(sp)
library(raster)

Warning: package 'raster' was built under R version 4.3.3

library(devtools)
library(RCurl)
library(sdmTMB)
library(terra)

Warning: package 'terra' was built under R version 4.3.3

library(ncdf4)
library(chron)
library(viridis)
library(readxl)

library(ggsidekick)
theme_set(theme_sleek()) # devtools::install_github("seananderson/ggsidekick") # not on CRAN

# Source code for map plots
devtools::source_url("https://raw.githubusercontent.com/maxlindmark/pred-prey-overlap/main/R/functions/map-plot.R")

Warning: package 'sf' was built under R version 4.3.3

# Set path
home <- here::here()

Read data and depth-raster

# Read data
d <- readr::read_csv("https://raw.githubusercontent.com/maxlindmark/spatial-metabolic-index/main/data/clean/catch_clean.csv") |>
  rename(X = x, Y = y)

Make the grid with depth

First make a grid for the biomass data, then subset that based on the extend of the stomach data

x <- d$X
y <- d$Y
z <- chull(x, y)

coords <- cbind(x[z], y[z])

coords <- rbind(coords, coords[1, ])

plot(coords[, 1] ~ coords[, 2]) # plot data

sp_poly <- sp::SpatialPolygons(
  list(sp::Polygons(list(sp::Polygon(coords)), ID = 1))
)

sp_poly_df <- sp::SpatialPolygonsDataFrame(sp_poly,
  data = data.frame(ID = 1)
)
cell_width <- 3

pred_grid <- expand.grid(
  X = seq(min(d$X), max(d$X), cell_width),
  Y = seq(min(d$Y), max(d$Y), cell_width),
  year = c(1993:2023)
)

ggplot(pred_grid |> filter(year == 2019), aes(X, Y)) +
  geom_point(size = 0.1) +
  theme_void() +
  coord_sf()

sp::coordinates(pred_grid) <- c("X", "Y")

inside <- !is.na(sp::over(pred_grid, as(sp_poly_df, "SpatialPolygons")))

pred_grid <- pred_grid[inside, ]

pred_grid <- as.data.frame(pred_grid)

ggplot(data = filter(pred_grid, year == 1999), aes(X * 1000, Y * 1000)) +
  geom_point(size = 0.001, alpha = 0.5) +
  NULL

plot_map +
  geom_point(data = filter(pred_grid, year == 1999), aes(X * 1000, Y * 1000), size = 0.001, alpha = 0.5) +
  NULL

Warning: Removed 2665 rows containing missing values or values outside the scale range
(`geom_point()`).

# Add lat and lon
# Need to go from UTM to lat long for this one...
# https://stackoverflow.com/questions/30018098/how-to-convert-utm-coordinates-to-lat-and-long-in-r
xy <- as.matrix(pred_grid |> dplyr::select(X, Y) |> mutate(X = X * 1000, Y = Y * 1000))
v <- vect(xy, crs = "+proj=utm +zone=33 +datum=WGS84  +units=m")
y <- project(v, "+proj=longlat +datum=WGS84")
lonlat <- geom(y)[, c("x", "y")]

pred_grid$lon <- lonlat[, 1]
pred_grid$lat <- lonlat[, 2]

ggplot(filter(pred_grid, year == 1999), aes(lon, lat)) +
  geom_point()

# Add depth now to remove islands and remaining land
# https://gis.stackexchange.com/questions/411261/read-multiple-layers-raster-from-ncdf-file-using-terra-package
# https://emodnet.ec.europa.eu/geoviewer/
dep_raster <- terra::rast(paste0(home, "/data/covariates/depth/Mean depth natural colour (with land).nc"))
class(dep_raster)

[1] "SpatRaster"
attr(,"package")
[1] "terra"

crs(dep_raster, proj = TRUE)

[1] "+proj=longlat +datum=WGS84 +no_defs"

plot(dep_raster)

pred_grid$depth <- terra::extract(dep_raster, pred_grid |> dplyr::select(lon, lat))$elevation

ggplot(pred_grid, aes(lon, lat, color = depth * -1)) +
  geom_point()

pred_grid$depth <- pred_grid$depth * -1

pred_grid <- pred_grid |> drop_na(depth)

pred_grid |>
  filter(year == 1999) |>
  drop_na(depth) |>
  # mutate(water = ifelse(depth < 0.00000001, "N", "Y")) |>
  ggplot(aes(X * 1000, Y * 1000, fill = depth)) +
  geom_raster() +
  NULL

plot_map +
  geom_point(data = pred_grid, aes(X * 1000, Y * 1000), size = 0.001) +
  geom_sf()

Warning: Removed 48577 rows containing missing values or values outside the scale range
(`geom_point()`).

plot_map +
  geom_raster(data = filter(pred_grid, year == 1999), aes(X * 1000, Y * 1000, fill = depth), size = 0.001) +
  geom_sf()

Warning in geom_raster(data = filter(pred_grid, year == 1999), aes(X * 1000, :
Ignoring unknown parameters: `size`

Warning: Removed 1567 rows containing missing values or values outside the scale range
(`geom_raster()`).

Add month_year variable for matching with raster layers

dat <- replicate_df(pred_grid, "quarter", c(1, 4)) |>
  mutate(
    month = ifelse(quarter == 1, 2, 11), # most common months
    month_year = paste(month, year, sep = "_")
  )

Add ICES areas

# https://stackoverflow.com/questions/34272309/extract-shapefile-value-to-point-with-r
# https://gis.ices.dk/sf/
shape <- shapefile(paste0(home, "/data/shapefiles/ICES-StatRec-mapto-ICES-Areas/StatRec_map_Areas_Full_20170124.shp"))
head(shape)

  OBJECTID ID ICESNAME SOUTH WEST NORTH EAST AREA_KM2 stat_x stat_y Area_27
1        1 47     47A0  59.0  -44  59.5  -43     3178  -43.5  59.25  14.b.2
2        2 48     48A0  59.5  -44  60.0  -43     3132  -43.5  59.75  14.b.2
3        3 49     49A0  60.0  -44  60.5  -43     3085  -43.5  60.25  14.b.2
4        4 50     50A0  60.5  -44  61.0  -43     3038  -43.5  60.75  14.b.2
5        5 51     51A0  61.0  -44  61.5  -43     2991  -43.5  61.25  14.b.2
6        6 52     52A0  61.5  -44  62.0  -43     2944  -43.5  61.75  14.b.2
          Perc      MaxPer RNDMaxPer AreasList Shape_Leng Shape_Area
1 100.00000000 100.0000000       100    14.b.2          3        0.5
2  84.12674145  84.1267414        84    14.b.2          3        0.5
3  24.99803694  24.9980369        25    14.b.2          3        0.5
4  11.97744244  11.9774424        12    14.b.2          3        0.5
5   3.89717625   3.8971762         4    14.b.2          3        0.5
6   0.28578428   0.2857843         0    14.b.2          3        0.5

pts <- SpatialPoints(cbind(dat$lon, dat$lat),
  proj4string = CRS(proj4string(shape))
)

dat$subdiv <- over(pts, shape)$Area_27

# Rename subdivisions to the more common names and do some more filtering (by sub div and area)
sort(unique(dat$subdiv))

[1] "3.b.23"   "3.d.24"   "3.d.25"   "3.d.26"   "3.d.27"   "3.d.28.1" "3.d.28.2"

dat <- dat |>
  mutate(
    sub_div = factor(subdiv),
    sub_div = fct_recode(subdiv,
      "24" = "3.d.24",
      "25" = "3.d.25",
      "26" = "3.d.26",
      "27" = "3.d.27",
      "28" = "3.d.28.1",
      "28" = "3.d.28.2",
      "29" = "3.d.29"
    ),
    sub_div = as.character(sub_div)
  ) |>
  filter(sub_div %in% c("24", "25", "26", "27", "28", 2)) |>
  filter(lat > 54 & lat < 59 & lon < 22)

Warning: There was 1 warning in `.fun()`.
ℹ In argument: `sub_div = fct_recode(...)`.
Caused by warning:
! Unknown levels in `f`: 3.d.29

# Add ICES rectangles
dat$ices_rect <- mapplots::ices.rect2(lon = dat$lon, lat = dat$lat)

dat <- dat |> dplyr::select(-subdiv)

Add Saduria

saduria <- terra::rast(paste0(home, "/data/covariates/saduria/FWBiomassm_raster_19812019presHighweightcor_no0_newZi.tif"))

WGS84 <- "+proj=longlat +datum=WGS84"

saduria_latlon <- terra::project(saduria, WGS84)

density_saduria <- terra::extract(saduria_latlon, dat |> dplyr::select(lon, lat))

dat$saduria <- density_saduria$FWBiomassm_raster_19812019presHighweightcor_no0_newZi

Add pelagic

# Read data on rectangle level
spr <- read_xlsx(paste0(home, "/data/covariates/pelagic/N and B per Rect. 1991-2023.xlsx"),
  sheet = 4
) |>
  rename(
    ices_rect = RECT,
    year = ANNUS
  ) |>
  mutate_at(vars(`1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`), ~ replace_na(., 0)) |> # I need to replace NA with 0, else I can't sum! According to Olavi who sent the data, NA means 0 and nothing else. Rectangle*year combinations that do not have information about biomass are simply not included in this data
  mutate(
    ices_rect = as.factor(ices_rect),
    Species = "Sprat",
    biomass_spr = `1` + `2` + `3` + `4` + `5` + `6` + `7` + `8`,
    id_r = paste(ices_rect, year, sep = "_")
  ) |>
  filter(year >= 1993) |>
  dplyr::select(year, ices_rect, biomass_spr) |>
  summarise(biomass_spr = mean(biomass_spr), .by = c(ices_rect, year))

her <- read_xlsx(paste0(home, "/data/covariates/pelagic/N and B per Rect. 1991-2023.xlsx"),
  sheet = 3
) |>
  rename(
    ices_rect = RECT,
    year = ANNUS
  ) |>
  mutate_at(vars(`1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`), ~ replace_na(., 0)) |> # I need to replace NA with 0, else I can't sum! According to Olavi who sent the data, NA means 0 and nothing else. Rectangle*year combinations that do not have information about biomass are simply not included in this data
  mutate(
    ices_rect = as.factor(ices_rect),
    Species = "Sprat",
    biomass_her = `1` + `2` + `3` + `4` + `5` + `6` + `7` + `8`,
    id_r = paste(ices_rect, year, sep = "_")
  ) |>
  filter(year >= 1993) |>
  dplyr::select(year, ices_rect, biomass_her) |>
  summarise(biomass_her = mean(biomass_her), .by = c(ices_rect, year))

pelagics <- left_join(spr, her, by = c("year", "ices_rect"))

dat <- dat |> tidylog::left_join(pelagics, by = c("year", "ices_rect"))

# If there are no data in a specific rectangle, use the sub-division mean. If no values in the sub div, use annual mean
dat <- dat |>
  mutate(
    biomass_spr_sd_mean = mean(biomass_spr, na.rm = TRUE),
    biomass_her_sd_mean = mean(biomass_her, na.rm = TRUE),
    .by = c(sub_div, year)
  ) |>
  mutate(
    biomass_spr = ifelse(is.na(biomass_spr), biomass_spr_sd_mean, biomass_spr),
    biomass_her = ifelse(is.na(biomass_her), biomass_her_sd_mean, biomass_her)
  ) |>
  mutate(
    biomass_spr_yr_mean = mean(biomass_spr, na.rm = TRUE),
    biomass_her_yr_mean = mean(biomass_her, na.rm = TRUE),
    .by = c(year)
  ) |>
  mutate(
    biomass_spr = ifelse(is.na(biomass_spr), biomass_spr_yr_mean, biomass_spr),
    biomass_her = ifelse(is.na(biomass_her), biomass_her_yr_mean, biomass_her)
  )

Add environmental covariates

Oxygen

covPath <- paste0(home, "/data/covariates")

# Source:
# https://data.marine.copernicus.eu/product/BALTICSEA_ANALYSISFORECAST_BGC_003_007/download?dataset=cmems_mod_bal_bgc_anfc_P1M-m_202311
# https://data.marine.copernicus.eu/product/BALTICSEA_MULTIYEAR_BGC_003_012/download?dataset=cmems_mod_bal_bgc_my_P1M-m_202303
# Print details
print(nc_open(paste(covPath, "oxygen", "cmems_mod_bal_bgc_my_P1M-m_1718018848983.nc", sep = "/")))

File /Users/maxlindmark/Dropbox/Max work/R/pred-prey-overlap/data/covariates/oxygen/cmems_mod_bal_bgc_my_P1M-m_1718018848983.nc (NC_FORMAT_NETCDF4):

     1 variables (excluding dimension variables):
        float o2b[longitude,latitude,time]   (Contiguous storage)  
            units: mmol m-3
            _FillValue: NaN
            standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
            long_name: Dissolved Oxygen Concentration

     3 dimensions:
        latitude  Size:404 
            standard_name: latitude
            long_name: Latitude
            units: degrees_north
            unit_long: Degrees North
            axis: Y
            valid_min: 53.0082969665527
            valid_max: 65.8909912109375
        longitude  Size:505 
            standard_name: longitude
            long_name: Longitude
            units: degrees_east
            unit_long: Degrees East
            axis: X
        time  Size:348 
            standard_name: time
            long_name: Time
            units: seconds since 1970-01-01 00:00:00
            calendar: gregorian
            axis: T

    12 global attributes:
        Conventions: CF-1.11
        title: CMEMS ERGOM monthly integrated model fields
        institution: Baltic MFC, PU Danish Meteorological Institute
        source: CMEMS BAL MFC NEMO model output converted to NetCDF
        history: Fri Dec 16 10:51:30 2022: cdo -z zip_1 --sortname copy /net/isilon/ifs/arch/home/iri/BALMFC/Nemo4.0/BALMFCMYP2022_univariateSST//BAL-ERGOM_BGC-DailyMeans-19930101.nc tmp_19930101.nc
        contact: servicedesk.cmems@mercator-ocean.eu
        references: https://marine.copernicus.eu/
        comment: Data on cropped native product grid. Horizontal velocities destagged
        subset:source: ARCO data downloaded from the Marine Data Store using the MyOcean Data Portal
        subset:productId: BALTICSEA_MULTIYEAR_BGC_003_012
        subset:datasetId: cmems_mod_bal_bgc_my_P1M-m_202303
        subset:date: 2024-06-10T11:27:28.983Z

oxy_tibble <- tidync(paste(covPath, "oxygen",
  "cmems_mod_bal_bgc_my_P1M-m_1718018848983.nc",
  sep = "/"
)) |>
  hyper_tibble() |>
  mutate(date = as_datetime(time, origin = "1970-01-01")) |>
  mutate(
    month = month(date),
    day = day(date),
    year = year(date),
    month_year = paste(month, year, sep = "_"),
    oxy = o2b * 0.0223909
  )

# Now do recent data (forecast)
print(nc_open(paste(covPath, "oxygen", "cmems_mod_bal_bgc_anfc_P1M-m_1718022118172.nc", sep = "/")))

File /Users/maxlindmark/Dropbox/Max work/R/pred-prey-overlap/data/covariates/oxygen/cmems_mod_bal_bgc_anfc_P1M-m_1718022118172.nc (NC_FORMAT_NETCDF4):

     1 variables (excluding dimension variables):
        float o2b[longitude,latitude,time]   (Contiguous storage)  
            units: mmol m-3
            _FillValue: NaN
            standard_name: mole_concentration_of_dissolved_molecular_oxygen_in_sea_water
            long_name: Dissolved Oxygen Concentration

     3 dimensions:
        latitude  Size:323 
            standard_name: latitude
            long_name: Latitude
            units: degrees_north
            unit_long: Degrees North
            axis: Y
            valid_min: 53.0082969665527
            valid_max: 65.8909912109375
        longitude  Size:456 
            standard_name: longitude
            long_name: Longitude
            units: degrees_east
            unit_long: Degrees East
            axis: X
        time  Size:30 
            standard_name: time
            long_name: Time
            units: seconds since 1970-01-01 00:00:00
            calendar: gregorian
            axis: T

    11 global attributes:
        Conventions: CF-1.11
        title: CMEMS ERGOM monthly integrated model fields
        institution: Baltic MFC, PU Swedish Meteorological and Hydrological Institute
        source: CMEMS BAL MFC NEMO model output converted to NetCDF
        contact: servicedesk.cmems@mercator-ocean.eu
        references: https://marine.copernicus.eu/
        comment: Data on cropped native product grid. Horizontal velocities destagged
        subset:source: ARCO data downloaded from the Marine Data Store using the MyOcean Data Portal
        subset:productId: BALTICSEA_ANALYSISFORECAST_BGC_003_007
        subset:datasetId: cmems_mod_bal_bgc_anfc_P1M-m_202311
        subset:date: 2024-06-10T12:21:58.172Z

oxy_tibble_new <- tidync(paste(covPath, "oxygen",
  "cmems_mod_bal_bgc_anfc_P1M-m_1718022118172.nc",
  sep = "/"
)) |>
  hyper_tibble() |>
  mutate(date = as_datetime(time, origin = "1970-01-01")) |>
  mutate(
    month = month(date),
    day = day(date),
    year = year(date),
    month_year = paste(month, year, sep = "_"),
    oxy = o2b * 0.0223909
  ) |>
  filter(year > 2021)

oxy_tibble <- bind_rows(oxy_tibble, oxy_tibble_new)

# Loop through all year combos, extract the temperatures at the data locations
oxy_list <- list()

for (i in unique(dat$month_year)) {
  d_sub <- filter(dat, month_year == i)
  oxy_tibble_sub <- filter(oxy_tibble, month_year == i)

  # Convert to raster
  ggplot(oxy_tibble_sub, aes(longitude, latitude)) +
    geom_point(size = 0.1)

  oxy_raster <- as_spatraster(oxy_tibble_sub,
    xycols = 2:3,
    crs = "WGS84", digits = 3
  )

  ggplot() +
    geom_spatraster(data = oxy_raster$oxy, aes(fill = oxy)) +
    scale_fill_viridis(option = "magma") +
    ggtitle(i)

  # Extract from raster
  d_sub$oxy <- terra::extract(
    oxy_raster$oxy,
    d_sub |> dplyr::select(lon, lat)
  )$oxy

  # Save
  oxy_list[[i]] <- d_sub
}

d_oxy <- bind_rows(oxy_list)

Temperature & Salinity

# Source
# https://data.marine.copernicus.eu/product/BALTICSEA_MULTIYEAR_PHY_003_011/description
# https://data.marine.copernicus.eu/product/BALTICSEA_ANALYSISFORECAST_PHY_003_006/description
# Print details
print(nc_open(paste(covPath, "temperature_salinity", "cmems_mod_bal_phy_my_P1M-m_1718087629163.nc", sep = "/")))

File /Users/maxlindmark/Dropbox/Max work/R/pred-prey-overlap/data/covariates/temperature_salinity/cmems_mod_bal_phy_my_P1M-m_1718087629163.nc (NC_FORMAT_NETCDF4):

     2 variables (excluding dimension variables):
        float bottomT[longitude,latitude,time]   (Contiguous storage)  
            units: degrees_C
            _FillValue: NaN
            standard_name: sea_water_potential_temperature_at_sea_floor
            long_name: Sea floor potential temperature
        float sob[longitude,latitude,time]   (Contiguous storage)  
            units: 0.001
            _FillValue: NaN
            standard_name: sea_water_salinity_at_sea_floor
            long_name: Sea water salinity at sea floor

     3 dimensions:
        latitude  Size:323 
            standard_name: latitude
            long_name: Latitude
            units: degrees_north
            unit_long: Degrees North
            axis: Y
            valid_min: 53.0082969665527
            valid_max: 65.8909912109375
        longitude  Size:418 
            standard_name: longitude
            long_name: Longitude
            units: degrees_east
            unit_long: Degrees East
            axis: X
        time  Size:348 
            standard_name: time
            long_name: Time
            units: seconds since 1970-01-01 00:00:00
            calendar: gregorian
            axis: T

    11 global attributes:
        Conventions: CF-1.11
        title: CMEMS NEMO monthly integrated model fields
        institution: Baltic MFC, PU Danish Meteorological Institute
        source: CMEMS BAL MFC NEMO model output converted to NetCDF
        contact: servicedesk.cmems@mercator-ocean.eu
        references: https://marine.copernicus.eu/
        comment: Data on cropped native product grid. Horizontal velocities destagged
        subset:source: ARCO data downloaded from the Marine Data Store using the MyOcean Data Portal
        subset:productId: BALTICSEA_MULTIYEAR_PHY_003_011
        subset:datasetId: cmems_mod_bal_phy_my_P1M-m_202303
        subset:date: 2024-06-11T06:33:49.163Z

st_tibble <- tidync(paste(covPath, "temperature_salinity",
  "cmems_mod_bal_phy_my_P1M-m_1718087629163.nc",
  sep = "/"
)) |>
  hyper_tibble() |>
  mutate(date = as_datetime(time, origin = "1970-01-01")) |>
  mutate(
    month = month(date),
    day = day(date),
    year = year(date),
    month_year = paste(month, year, sep = "_")
  )

# Now do recent data (forecast)
print(nc_open(paste(covPath, "temperature_salinity", "cmems_mod_bal_phy_anfc_P1M-m_1718087127439.nc", sep = "/")))

File /Users/maxlindmark/Dropbox/Max work/R/pred-prey-overlap/data/covariates/temperature_salinity/cmems_mod_bal_phy_anfc_P1M-m_1718087127439.nc (NC_FORMAT_NETCDF4):

     2 variables (excluding dimension variables):
        float bottomT[longitude,latitude,time]   (Contiguous storage)  
            units: degrees_C
            _FillValue: NaN
            standard_name: sea_water_potential_temperature_at_sea_floor
            long_name: Sea water potential temperature at sea floor (given for depth comprise between 0 and 500m)
        float sob[longitude,latitude,time]   (Contiguous storage)  
            units: 0.001
            _FillValue: NaN
            standard_name: sea_water_salinity_at_sea_floor
            long_name: Sea water salinity at sea floor

     3 dimensions:
        latitude  Size:395 
            standard_name: latitude
            long_name: Latitude
            units: degrees_north
            unit_long: Degrees North
            axis: Y
            valid_min: 53.0082969665527
            valid_max: 65.8909912109375
        longitude  Size:426 
            standard_name: longitude
            long_name: Longitude
            units: degrees_east
            unit_long: Degrees East
            axis: X
        time  Size:30 
            standard_name: time
            long_name: Time
            units: seconds since 1970-01-01 00:00:00
            calendar: gregorian
            axis: T

    11 global attributes:
        Conventions: CF-1.11
        title: CMEMS NEMO monthly integrated model fields
        institution: Baltic MFC, PU Swedish Meteorological and Hydrological Institute
        source: CMEMS BAL MFC NEMO model output converted to NetCDF
        contact: servicedesk.cmems@mercator-ocean.eu
        references: https://marine.copernicus.eu/
        comment: Data on cropped native product grid. Horizontal velocities destagged
        subset:source: ARCO data downloaded from the Marine Data Store using the MyOcean Data Portal
        subset:productId: BALTICSEA_ANALYSISFORECAST_PHY_003_006
        subset:datasetId: cmems_mod_bal_phy_anfc_P1M-m_202311
        subset:date: 2024-06-11T06:25:27.439Z

st_tibble_new <- tidync(paste(covPath, "temperature_salinity",
  "cmems_mod_bal_phy_anfc_P1M-m_1718087127439.nc",
  sep = "/"
)) |>
  hyper_tibble() |>
  mutate(date = as_datetime(time, origin = "1970-01-01")) |>
  mutate(
    month = month(date),
    day = day(date),
    year = year(date),
    month_year = paste(month, year, sep = "_")
  ) |>
  filter(year > 2021)

st_tibble <- bind_rows(st_tibble, st_tibble_new)

# Loop through all year combos, extract the temperatures at the data locations
st_list <- list()

for (i in unique(dat$month_year)) {
  d_sub <- filter(dat, month_year == i)
  st_tibble_sub <- filter(st_tibble, month_year == i)

  # Convert to raster
  ggplot(st_tibble_sub, aes(longitude, latitude)) +
    geom_point(size = 0.1)

  st_raster <- as_spatraster(st_tibble_sub,
    xycols = 3:4,
    crs = "WGS84", digits = 3
  )

  ggplot() +
    geom_spatraster(data = st_raster$bottomT, aes(fill = bottomT)) +
    scale_fill_viridis(option = "magma") +
    ggtitle(i)

  ggplot() +
    geom_spatraster(data = st_raster$sob, aes(fill = sob)) +
    scale_fill_viridis(option = "magma") +
    ggtitle(i)

  # Extract from raster
  d_sub$temp <- terra::extract(
    st_raster$bottomT,
    d_sub |> dplyr::select(lon, lat)
  )$bottomT

  d_sub$salinity <- terra::extract(
    st_raster$sob,
    d_sub |> dplyr::select(lon, lat)
  )$sob

  # Save
  st_list[[i]] <- d_sub
}

d_st <- bind_rows(st_list)

env_dat <- d_st |>
  left_join(d_oxy |> dplyr::select(-X, -Y, -depth, -quarter, -year, -month),
    by = c("month_year", "lon", "lat")
  ) |>
  dplyr::select(month_year, lon, lat, temp, salinity, oxy)

# Now join these data with the full_dat
dat_full <- left_join(dat, env_dat, by = c("month_year", "lon", "lat")) |>
  drop_na(temp, salinity, oxy)

Save

# Remove variables and save
pred_grid_93_08 <- dat_full |> filter(year <= 2008)
pred_grid_09_23 <- dat_full |> filter(year >= 2009)

write_csv(pred_grid_93_08, file = paste0(home, "/data/clean/pred_grid_(1_2).csv"))
write_csv(pred_grid_09_23, file = paste0(home, "/data/clean/pred_grid_(2_2).csv"))